Sangwoo Moon

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As high-speed networks are becoming commonplace, it is increasingly challenging to prevent the attack attempts at the edge of the Internet. While many high-performance intrusion detection systems (IDSes) employ dedicated network processors or special memory to meet the demanding performance requirements, it often increases the cost and limits functional(More)
This paper presents a new hybrid dimensionality reduction method to seek projection through optimization of both structural risk (supervised criterion) and data independence (unsupervised criterion). Classification accuracy is used as a metric to evaluate the performance of the method. By minimizing the structural risk, projection originated from the(More)
This paper presents face recognition with activity level fusion of visible and thermal image using multiscale decomposition. Image fusion combines images with different features to obtain a single composite image with extended information for better recognition performance. The proposed fusion technique adoptively controls the fusion ratio between visible(More)
In this paper, we address the problem of associating mobile stations with base stations (BSs) in an energy-efficient manner. We take the population game approach, which allows tractable analysis of many selfish mobiles without growing mathematical complexity, where our study provides two practical implications on energy-efficient BS associations: (i) how to(More)
Optimal CSMA, which is fully distributed wireless MAC theory, has provided a rule of dynamically adapting CSMA parameters according to some theoretically developed principles, and has reported to offer nice analytical guarantees on throughput and fairness. Despite a couple of research efforts that transfer Optimal CSMA to practical protocols, e.g., O-DCF,(More)
We propose a new conditionally factorized covariance intersection (CI) algorithm for performing partial state decentralized data fusion (DDF). This is relevant for sensor networks where platforms must deal with mixed heterogeneous state estimation problems, e.g. due to coupling between uncertainties in shared subsets of externally monitored process states(More)
Compressed Sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It is promising that CS can be utilized in environments where the signal acquisition process is extremely difficult or costly, e.g., a resource-constrained environment like the(More)